# Thread: Method of Moment Estimators

1. ## Method of Moment Estimators

Does anyone know an online link or website where I can see the derivation of MMEs and MLEs of different discrete and continuous distributions? I am being unable to understand my book.

Thank you

2. Originally Posted by harish21
Does anyone know an online link or website where I can see the derivation of MMEs and MLEs of different discrete and continuous distributions? I am being unable to understand my book.

Thank you
If you have a sample of size n $(k_1,k_2,...,k_n)$ from a distribution with pdf $p_X$ or $(f_Y)$ and paramater $\theta$, you form
$L(\theta)=\prod_{i=1}^np_X(k_i;\theta)$ or a similar form for the continuous case.

The MLE for $\theta, \theta_e$, is any value of $\theta$ which maximizes $L(\theta)$

To maximize something we want to take the derivative and set it equal to zero. Often with MLE's it is easier to take the derivative of the $ln(L(\theta))$.

The method of moments estimators come from matching the sample moments $(\frac{1}{n}\sum_{i=1}^nk_i^r)$ with the theoretical moments $E(X^r)$=function of the parameters.

If you have specific examples of ones you are having trouble with, you can post them and I can show you how to find these estimates. I think you are probably just as capable as anyone else of finding online proofs of these though.